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As Wagstaff [60] suggests for binary health variables, we normalize the CI by dividing it through by the reciprocal of the mean of the variable in question (1-μ): W C I=frac{2}{ nmu left 1-mu right)}{displeft 1-musum_{i=1}^n}{y}_i{right1.
For binary health outcomes (e.g. use or non-use of a health service), the feasible bounds of the concentration index narrow as the prevalence rate rises.
Odds ratios and 95% confidence intervals are estimated for binary health outcomes using logistic regression to compare exposed and unexposed populations while controlling for age, gender, education and race/ethnicity.
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Logistic regression analyses were used for the binary health-related outcomes, that is smoking status, self-checking blood glucose level and knowledge of type of diabetes.
In addition, for a health condition or a binary health risk factor, we may define either the existence or absence as zero or one and again have two possible intuitive "zero points".
Logistic regression was used to analyse the binary health outcomes.
The first is the 'explained' component, in which β k is the coefficient of each determinant calculated using generalized linear models with a binomial distribution and identity link on the binary health outcome, is the mean of each determinant, µ is the mean of the binary health outcome and ck is the concentration index for each determinant.
Descriptive, multivariable and mediation analyses were conducted separately for the three binary health-seeking behaviour outcomes available for each illness in Stata SE13.
This finding likely reflects the greater comfort with random-effects models for binary outcomes in health research, as these models are used much more frequently now and are readily available in most mainstream statistical packages.
For the dependent variable, self-rated health, we used multilevel models for binary responses.
The proposed RBP for binary and continuous markers/exposures can be extended to health economics studies.
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